Runnable Class — langchain Architecture

Source Code

libs/core/langchain_core/runnables/base.py lines 124–2583

class Runnable(ABC, Generic[Input, Output]):
    """A unit of work that can be invoked, batched, streamed, transformed and composed.

    Key Methods
    ===========

    - `invoke`/`ainvoke`: Transforms a single input into an output.
    - `batch`/`abatch`: Efficiently transforms multiple inputs into outputs.
    - `stream`/`astream`: Streams output from a single input as it's produced.
    - `astream_log`: Streams output and selected intermediate results from an
        input.

    Built-in optimizations:

    - **Batch**: By default, batch runs invoke() in parallel using a thread pool
        executor. Override to optimize batching.

    - **Async**: Methods with `'a'` prefix are asynchronous. By default, they execute
        the sync counterpart using asyncio's thread pool.
        Override for native async.

    All methods accept an optional config argument, which can be used to configure
    execution, add tags and metadata for tracing and debugging etc.

    Runnables expose schematic information about their input, output and config via
    the `input_schema` property, the `output_schema` property and `config_schema`
    method.

    Composition
    ===========

    Runnable objects can be composed together to create chains in a declarative way.

    Any chain constructed this way will automatically have sync, async, batch, and
    streaming support.

    The main composition primitives are `RunnableSequence` and `RunnableParallel`.

    **`RunnableSequence`** invokes a series of runnables sequentially, with
    one Runnable's output serving as the next's input. Construct using
    the `|` operator or by passing a list of runnables to `RunnableSequence`.

    **`RunnableParallel`** invokes runnables concurrently, providing the same input
    to each. Construct it using a dict literal within a sequence or by passing a
    dict to `RunnableParallel`.


    For example,

    ```python
    from langchain_core.runnables import RunnableLambda

    # A RunnableSequence constructed using the `|` operator
    sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
    sequence.invoke(1)  # 4
    sequence.batch([1, 2, 3])  # [4, 6, 8]


    # A sequence that contains a RunnableParallel constructed using a dict literal
    sequence = RunnableLambda(lambda x: x + 1) | {
        "mul_2": RunnableLambda(lambda x: x * 2),
        "mul_5": RunnableLambda(lambda x: x * 5),
    }
    sequence.invoke(1)  # {'mul_2': 4, 'mul_5': 10}
    ```

    Standard Methods
    ================

    All `Runnable`s expose additional methods that can be used to modify their
    behavior (e.g., add a retry policy, add lifecycle listeners, make them
    configurable, etc.).

    These methods will work on any `Runnable`, including `Runnable` chains
    constructed by composing other `Runnable`s.
    See the individual methods for details.

    For example,

    ```python
    from langchain_core.runnables import RunnableLambda